1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was 87b8bf86ada0300fd651f355dad57b03e6b15b4d.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below we plot cumulative case count on a log scale by continent. Note that “Other” relates to ships.

Reported Cases by Continent

Reported Cases by Continent

Below we plot the cumulative deaths by country on a log scale:

Reported Deaths by Continent

Reported Deaths by Continent

4 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

5 Results

5.1 Current \(R_{t,m}\) estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.1.0.1 Cases

5.1.1 Deaths

5.2 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 50 in the last week.

5.2.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Serbia deaths 225 2021-01-09 0.7 0.8 0.9
Pakistan deaths 333 2021-01-09 0.7 0.8 0.9
Iran deaths 662 2021-01-09 0.7 0.8 0.9
Moldova deaths 110 2021-01-09 0.7 0.8 1.0
Belgium deaths 394 2021-01-09 0.7 0.8 0.9
Azerbaijan deaths 187 2021-01-09 0.7 0.8 1.0
Croatia deaths 326 2021-01-09 0.7 0.8 0.9
Georgia deaths 178 2021-01-09 0.7 0.8 1.0
Slovenia deaths 199 2021-01-09 0.7 0.9 1.0
Turkey deaths 1,336 2021-01-09 0.8 0.9 0.9

5.2.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Bhutan cases 57 2021-01-09 0.4 0.5 0.7
Mali cases 339 2021-01-09 0.5 0.6 0.7
Mauritania cases 752 2021-01-09 0.6 0.7 0.8
Australia cases 111 2021-01-09 0.6 0.7 0.9
Uzbekistan cases 348 2021-01-09 0.7 0.8 0.9
Moldova cases 3,399 2021-01-09 0.7 0.8 0.8
Serbia cases 14,221 2021-01-09 0.8 0.8 0.8
Palestine cases 6,414 2021-01-09 0.8 0.8 0.8
Azerbaijan cases 4,588 2021-01-09 0.7 0.8 0.9
Lithuania cases 12,781 2021-01-09 0.8 0.8 0.9

5.2.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Zambia deaths 72 2021-01-09 2.6 3.8 5.5
Zimbabwe deaths 106 2021-01-09 1.7 2.3 3.2
Guatemala deaths 172 2021-01-09 1.8 2.3 2.8
Sweden deaths 706 2021-01-09 1.5 1.6 1.7
El Salvador deaths 81 2021-01-09 1.2 1.6 2.0
Bolivia deaths 165 2021-01-09 1.3 1.5 1.8
United Kingdom deaths 6,318 2021-01-09 1.3 1.4 1.5
Ireland deaths 84 2021-01-09 1.1 1.4 1.7
Malaysia deaths 59 2021-01-09 1.0 1.4 1.8
Brazil deaths 6,906 2021-01-09 1.3 1.4 1.4

5.2.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Lesotho cases 1,997 2021-01-09 2.5 4.0 5.4
Malawi cases 1,594 2021-01-09 2.2 2.6 3.1
Zambia cases 5,337 2021-01-09 2.0 2.4 2.8
Mozambique cases 2,393 2021-01-09 2.1 2.3 2.5
Guyana cases 190 2021-01-09 1.8 2.2 2.7
Ireland cases 43,801 2021-01-09 1.7 2.1 2.6
Zimbabwe cases 6,008 2021-01-09 1.7 2.1 2.6
Cape Verde cases 433 2021-01-09 1.7 1.9 2.2
Cote d’Ivoire cases 919 2021-01-09 1.5 1.6 1.8
Comoros cases 286 2021-01-09 1.4 1.6 1.9

5.3 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

5.3.1 Cases

Risk Quadrants - Cases

5.3.2 Deaths

Risk Quadrants - Deaths

5.4 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

5.4.1 Africa

5.4.1.1 Algeria

5.4.1.2 Angola

5.4.1.3 Benin

5.4.1.4 Botswana

5.4.1.5 Burkina Faso

5.4.1.6 Burundi

5.4.1.7 Cameroon

5.4.1.8 Cape Verde

5.4.1.9 Central African Republic

5.4.1.10 Chad

5.4.1.11 Comoros

5.4.1.12 Congo

5.4.1.13 Cote d’Ivoire

5.4.1.14 Democratic Republic of Congo

5.4.1.15 Djibouti

5.4.1.16 Egypt

5.4.1.17 Equatorial Guinea

5.4.1.18 Eritrea

5.4.1.19 Eswatini

5.4.1.20 Ethiopia

5.4.1.21 Gabon

5.4.1.22 Gambia

5.4.1.23 Ghana

5.4.1.24 Guinea

5.4.1.25 Guinea-Bissau

5.4.1.26 Kenya

5.4.1.27 Lesotho

5.4.1.28 Liberia

5.4.1.29 Libya

5.4.1.30 Madagascar

5.4.1.31 Malawi

5.4.1.32 Mali

5.4.1.33 Mauritania

5.4.1.34 Mauritius

5.4.1.35 Morocco

5.4.1.36 Mozambique

5.4.1.37 Namibia

5.4.1.38 Niger

5.4.1.39 Nigeria

5.4.1.40 Rwanda

5.4.1.41 Senegal

5.4.1.42 Sierra Leone

5.4.1.43 Somalia

5.4.1.44 South Africa

5.4.1.45 South Sudan

5.4.1.46 Sudan

5.4.1.47 Togo

5.4.1.48 Tunisia

5.4.1.49 Uganda

5.4.1.50 Zambia

5.4.1.51 Zimbabwe

5.4.2 Asia

5.4.2.1 Afghanistan

5.4.2.2 Armenia

5.4.2.3 Azerbaijan

5.4.2.4 Bahrain

5.4.2.5 Bangladesh

5.4.2.6 Bhutan

5.4.2.7 Cambodia

5.4.2.8 China

5.4.2.9 Georgia

5.4.2.10 India

5.4.2.11 Indonesia

5.4.2.12 Iran

5.4.2.13 Iraq

5.4.2.14 Israel

5.4.2.15 Japan

5.4.2.16 Jordan

5.4.2.17 Kazakhstan

5.4.2.18 Kuwait

5.4.2.19 Kyrgyzstan

5.4.2.20 Lebanon

5.4.2.21 Malaysia

5.4.2.22 Maldives

5.4.2.23 Mongolia

5.4.2.24 Myanmar

5.4.2.25 Nepal

5.4.2.26 Oman

5.4.2.27 Pakistan

5.4.2.28 Palestine

5.4.2.29 Philippines

5.4.2.30 Qatar

5.4.2.31 Saudi Arabia

5.4.2.32 Singapore

5.4.2.33 South Korea

5.4.2.34 Sri Lanka

5.4.2.35 Syria

5.4.2.36 Taiwan

5.4.2.37 Tajikistan

5.4.2.38 Thailand

5.4.2.39 Turkey

5.4.2.40 United Arab Emirates

5.4.2.41 Uzbekistan

5.4.2.42 Vietnam

5.4.2.43 Yemen

5.4.3 Europe

5.4.3.1 Albania

5.4.3.2 Austria

5.4.3.3 Belarus

5.4.3.4 Belgium

5.4.3.5 Bosnia and Herzegovina

5.4.3.6 Bulgaria

5.4.3.7 Croatia

5.4.3.8 Cyprus

5.4.3.9 Czechia

5.4.3.10 Denmark

5.4.3.11 Estonia

5.4.3.12 Finland

5.4.3.13 France

5.4.3.14 Germany

5.4.3.15 Greece

5.4.3.16 Hungary

5.4.3.17 Ireland

5.4.3.18 Italy

5.4.3.19 Kosovo

5.4.3.20 Latvia

5.4.3.21 Lithuania

5.4.3.22 Luxembourg

5.4.3.23 Moldova

5.4.3.24 Montenegro

5.4.3.25 Netherlands

5.4.3.26 North Macedonia

5.4.3.27 Norway

5.4.3.28 Poland

5.4.3.29 Portugal

5.4.3.30 Romania

5.4.3.31 Russia

5.4.3.32 Serbia

5.4.3.33 Slovakia

5.4.3.34 Slovenia

5.4.3.35 Spain

5.4.3.36 Sweden

5.4.3.37 Switzerland

5.4.3.38 Ukraine

5.4.3.39 United Kingdom

5.4.4 North America

5.4.4.1 Canada

5.4.4.2 Costa Rica

5.4.4.3 Cuba

5.4.4.4 Dominican Republic

5.4.4.5 El Salvador

5.4.4.6 Guatemala

5.4.4.7 Haiti

5.4.4.8 Honduras

5.4.4.9 Jamaica

5.4.4.10 Mexico

5.4.4.11 Nicaragua

5.4.4.12 Panama

5.4.4.13 Trinidad and Tobago

5.4.4.14 United States

5.4.5 Oceania

5.4.5.1 Australia

5.4.5.2 New Zealand

5.4.5.3 Papua New Guinea

5.4.6 South America

5.4.6.1 Argentina

5.4.6.2 Bolivia

5.4.6.3 Brazil

5.4.6.4 Chile

5.4.6.5 Colombia

5.4.6.6 Ecuador

5.4.6.7 Guyana

5.4.6.8 Paraguay

5.4.6.9 Peru

5.4.6.10 Suriname

5.4.6.11 Uruguay

5.4.6.12 Venezuela

5.5 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020 [Online]. Available: https://ourworldindata.org/coronavirus. [Accessed: 17-Dec-2020]